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001014297 0247_ $$2datacite_doi$$a10.34734/FZJ-2023-03220
001014297 037__ $$aFZJ-2023-03220
001014297 041__ $$aEnglish
001014297 1001_ $$0P:(DE-Juel1)194707$$aNieto, Nicolas$$b0$$eCorresponding author
001014297 1112_ $$aOrganization for Human Brain Mapping (OHBM)$$cMontreal$$d2023-07-22 - 2023-07-26$$wCanada
001014297 245__ $$aJuHarmonize: Leakage-free data harmonization
001014297 260__ $$c2023
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001014297 500__ $$aAcknowledgments: This study was supported by Helmholtz AI project DeGen and Helmholtz Portfolio Theme Supercomputing and Modeling for the Human Brain.
001014297 520__ $$aCombining datasets is desirable when building machine learning models. Differences in data acquisition present undesired variability undermining subsequent machine learning performance. Data harmonization methods such as ComBat can be employed, however, the requirement of test set labels causes data leakage and prevents real-world deployment. We propose a method called JuHarmonize that harmonizes data without those issues.
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001014297 7001_ $$0P:(DE-Juel1)185083$$aRaimondo, Federico$$b1
001014297 7001_ $$0P:(DE-Juel1)172843$$aPatil, Kaustubh$$b2
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